• 산업경영시스템학회지
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• 제47권2호
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• pp.116-123
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• 2024

In the manufacturing industry, dispatching systems play a crucial role in enhancing production efficiency and optimizing production volume. However, in dynamic production environments, conventional static dispatching methods struggle to adapt to various environmental conditions and constraints, leading to problems such as reduced production volume, delays, and resource wastage. Therefore, there is a need for dynamic dispatching methods that can quickly adapt to changes in the environment. In this study, we aim to develop an agent-based model that considers dynamic situations through interaction between agents. Additionally, we intend to utilize the Q-learning algorithm, which possesses the characteristics of temporal difference (TD) learning, to automatically update and adapt to dynamic situations. This means that Q-learning can effectively consider dynamic environments by sensitively responding to changes in the state space and selecting optimal dispatching rules accordingly. The state space includes information such as inventory and work-in-process levels, order fulfilment status, and machine status, which are used to select the optimal dispatching rules. Furthermore, we aim to minimize total tardiness and the number of setup changes using reinforcement learning. Finally, we will develop a dynamic dispatching system using Q-learning and compare its performance with conventional static dispatching methods.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 추계학술대회 학술발표 논문집 제14권 제2호
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• pp.303-308
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• 디지털융복합연구
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• 제17권4호
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• pp.19-27
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• 2019

본 연구는 교수체제개발에 기초하여 초등학생을 대상으로 한 놀이교육 프로그램을 개발하는데 목적이 있다. 이를 위해 ADDIE모형을 활용하여 협동학습과 프로젝트 기반의 과제 수행이 가능한 놀이교육 프로그램을 개발하였다. 내부 연구진, 교수설계 전문가, 내용 전문가의 검토 및 수정 과정을 거쳐 개발된 최종 놀이교육 프로그램은 디자인 및 계획 세우기, 놀이 공간 모형 만들기, 전시회 준비 및 작품 감상하기의 총 세 가지 모듈로 구성되어 있다. 학생들이 자신이 원하는 놀이공간을 또래 학생들과의 협업을 통해 프로젝트를 수행하는 것을 본 프로그램의 목적으로 세웠다. 개발된 놀이교육 프로그램은 충주시 소재 초등학교 5학년 학생을 대상으로 검증하였다. 놀이교육 프로그램이 종료된 시점에 13명의 학생을 대상으로 FGI를 시행하여 본 프로그램의 지속가능성 여부를 포함한 놀이교육 프로그램의 전반적인 가치를 측정하였다. 이러한 과정을 거쳐 놀이교육 프로그램의 효과 및 시사점을 도출하고 향후 놀이교육 및 프로젝트에 대한 발전 방안을 제시하였다.

• 대한간호학회지
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• 제48권6호
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• pp.743-753
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• 2018

Purpose: This study aimed to explore the effects of a community-based first and third Intergenerational Exchange Program (IGEP) on older adults' health-related quality of life (HRQoL), loneliness, depression, and walking speed, and on 4~5-year-old preschool children's learning-related social skills. Methods: This study employed a non-equivalent control group pre-post-test design. The experimental group included 42 older adults and 42 children who participated in the IGEP for 8 weeks, and the control group included 39 older adults. The experimental group participated in the IGEP once a week for 8 weeks. It comprised a traditional play program based on the intergroup contact theory. Results: Compared to the control group, there was a significant increase in scores on the HRQoL-Visual analogue scale (VAS) and a decrease in loneliness and depression in older adults in the experimental group (p<.05). Children who participated in the IGEP showed an improvement in their learning-related social skills (p<.001). Conclusion: These results confirm that the IGEP is an effective intervention to improve HRQoL-VAS, loneliness, and depression among older adults and learning-related social skills among preschool children in the community.

• 비교문화연구
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• 제38권
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• pp.461-500
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• 2015

This study aims to investigate the effective method to improve the competence of the Vocabulary by the image and listening towards the ELF. In the first part, we observed the problems and point improvement on learning vocabulary based on learner survey. In the second part, we analyzed two remarkable studies: - consistent and adapt method, communicational context - method based on the lexical, morphological semantical, notional and thematic field Then we proposed effective methods that are applicable to the vocabulary's learning in the class : - learning vocabulary by combining the words - learning vocabulary based on the meaning field - learning vocabulary as concrete characters - learning vocabulary by the descriptive character - learning vocabulary with the type "who am I?" - learning vocabulary by listening For teachers, one of the difficulties to the conduct of vocabulary course is that learners take passive position. Specifically, it is the teachers who play an important role because it runs in the direction of the course. However, learners do not show the active attitude for vocabulary lessons despite the course to take to improve their vocabulary skills. Therefore, teachers must prepare course materials that can both improve the competence of the vocabulary of learners and cause their interest or desire on the current vocabulary. This is why teachers should exploit various materials depending on the skill level of the learner vocabulary.

• 컴퓨터교육학회논문지
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• 제8권5호
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• pp.73-83
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• 2005

현재 초등학생들의 인터넷 이용률은 급격히 증가하고 있는 반면, 아이디와 비밀번호를 무분별하게 관리하고 있으며, 그에 따른 정보 유출의 심각성을 전혀 인식하지 못하고 있다. 본 논문에서는 초등학생 대상으로 개인정보 관리의 중요성을 인식시키기 위한 교수학습 방법을 설계하였다. 설계한 교수학습 방법은 역할놀이 모형을 기반으로 하였으며 초등학교 3학년을 대상으로 현장 수업에 적용하였다. 수업한 결과를 분석하여 설계한 교수학습 방법이 기존의 강의법보다 더 효과가 있음을 보인다.

• 컴퓨터교육학회논문지
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• 제5권2호
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• pp.29-37
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• 2002

본 연구는 웹 기반 PBL 환경에서 배경지식 수준과 메타인지 지원 도구 제공여부가 학습자의 PBL 활동에 어떠한 영향을 미치는가를 알아보기 위한 것이다. 본 연구를 위하여 웹기반 PBL 프로그램을 활용하였고 실험집단에 별도의 연구 제작된 메타인지 지원 도구를 포함시켰다. PBL수행과정에 있어서는 메타인지 지원 도구를 제공한 집단의 경우 평균이 3.82점 높아 유의도 수준 .05에서 의미있는 차이가 있는 것으로 나타났다. 또한 PBL 수행결과에 있어서 배경지식 수준과 메타인지 지원 도구의 제공여부간의 상호작용효과가 통계적으로 유의한 결과를 보였다. 결국 배경지식이 하위인 학습자 집단에 메타인지 지원 도구를 제공하여줌으로써 문제를 해결하고 결과 보고서를 작성하는데 효과적임을 알 수 있었다.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2004년도 학술발표논문집
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• pp.307-312
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• 2004

In our fast-moving information and knowledge society, skills and know-how rapidly become outdated. Virtual learning environments play a key role in meeting today's growing demand for customized educational and vocational training and lift-long teaming. The scope of multimedia-based and web-supported education is illustrated by means of an interdisciplinary multimedia project 'New Statistics' funded by the German government. The project output contains more than 70 learning modules covering the complete curriculum of an introductory statistics course. All modules are based on a statistical laboratory and on a multitude of Java applets, animations and case studies. The paper focuses on presenting the statistical laboratory and the applets. These components present the main project pillars and are particularly suitable for international use, independently from the original project framework. This article also demonstrates the application of Java applets and other multimedia developments from the educational world to official statistics for interactive presentation of statistical information.

• 전자통신동향분석
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• 제35권5호
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• pp.14-29
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• 2020

The 6G mobile communication system will become a backbone infrastructure around 2030 for the future digital world by providing distinctive services such as five-sense holograms, ultra-high reliability/low-latency, ultra-high-precision positioning, ultra-massive connectivity, and gigabit-per-second data rate for aerial and maritime terminals. The recent remarkable advances in machine learning (ML) technology have recognized its efficiency in wireless networking fields such as resource management and cell-configuration optimization. Further innovation in ML is expected to play an important role in solving new problems arising from 6G network management and service delivery. In contrast, an approach to apply ML to a physical-layer (PHY) target tackles the basic problems in radio links, such as overcoming signal distortion and interference. This paper reviews the methodologies of ML-based PHY, relevant industrial trends, and candiate technologies, including future research directions and standardization impacts.

• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.161-165
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• 2023

One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.